Multi-agents System Learning And Cooperation Research Of RoboCup

With the development of the computer technology, research on the theory and application of Multi-agent system( MAS) has become a hot spot of Artificial Intelligence. The Robot World Cup(RoboCup) is a typical MAS with characters such as dynamic environment, the co-existence of cooperation and competition among several agents, limited communication bandwidth, and the noisy environment. Based on this general test plat form, various theories of MAS can be researched and applied to many field.Considering the complexity of agent decision task in RoboCup, layer learning based on decision framework is designed. The framework divides the full decision task into several layers from high-level to low-level. To solve errors accumulation among layers, we adopt the improving layer's structure with a corresponding layer, which can be used to judge of decision-making information and correct inaccuracy information.In order to improve the intelligence of individual skills, the off-line learning method is adopted to learn the basic techniques such as ball interception.With the analysis of two different solutions,an improved dichotomy algorithm based on neural network and genetic algorithm is proposed to achieve ball interception. Q learning method is adopted to train basic skills of ball kicking.For the learning problem of agent team cooperation, the basic Q learning algorithm is extended introducing the concept of learning agent .And meanwhile,the agent can learn other agents' action policies through observing and counting the joint action, a concise but useful hypothesis is adopted to denote the optimal policies of other agents,the full joint probability of policies distribution guarantees the learning agent to choose optimal action.All the experiments are made under RoboCup simulation platform.The results have proved that the agent's learning method proposed in the paper can effectively improve the intelligence of agent decision in complex domain.